Pattern recognition involves a comparison of an unknown pattern with a large number of templates (or known patterns) to determine which template the unknown pattern is most similar to. A known, statistically optimal measure of similarity (for unknown patterns embedded in additive white noise) is the vector inner product (see, for example, C. W. Helstrom, Statistical Theory of Signal Detection, Pergamon Press, N.Y., 1968). The vector inner product between an unknown pattern and a template is obtained by computing the product of the value of each resolution element or pixel of the unknown pattern with the value of each corresponding resolution element or pixel of the template, and then summing all the products. The unknown pattern is said to match "best" with, or to be recognized as being, the template with which it has the largest vector inner product.
Pattern recognition problems, in which unknown patterns and templates are two-dimensional images, typically involve many (&gt;10.sup.3), high-resolution (&gt;10.sup.4 pixels) templates. Real-time (.about.10.sup.-4 second recognition time) problems of this type therefore require computational throughputs of&gt;10.sup.11 arithmetic operations per second (=number of pixels per template .times.number of templates.div.the recognition time). No available or projected digital electronic computers can process information at this rate.
Optical template matchers in which templates are stored in the form of two-dimensional Fourier-space transforms include those described by: D. Gabor in "Character Recognition by Holography" in Nature, 208, p. 422 (1965); J. T. La Macchia and D. L. White in "Coded Multiple Exposure Holograms," Applied Optics, 7, p. 91 (1968); J. R. Leger and S. H. Lee in "Hybrid Optical Processor for Pattern Recognition and Classification Using a Generalized Set of Pattern Functions," D. A. Gregory and H. K. Liu in "Large-Memory Real-Time Multi-channel Multiplexed Pattern Recognition," Applied Optics, 23, p. 4560 (1984); and D. Psaltis, M. A. Neifeld, and A. Yamamura in "Image Correlators Using Optical Memory Disks," 14, p. 429 (1989).
Additionally, in a paper by T. Jannson, H. M. Stoll, and C. Karaguleff ("The interconnectability of neuro-optic processors," Proceedings of the International Society for Optical Engineering, Vol. 698, p. 157 (1986)) there is described, on page 162, an optical volume-holographic architecture for computing inner products. The disclosure is, however, in the context of providing interconnects for an optical neural network.
It is one object of this invention to provide a method and apparatus that employs a three-dimensional volume holographic medium to provide an optical template matcher capable of storing a very large number of templates.
It is a further object of the invention to provide a compact (potentially less than 200 cubic inches), low-power (potentially less than 10 watts of prime electrical power) optical template matcher capable of executing at least 10.sup.11 arithmetic operations per second.